Python/Pandas：如何将字符串列表与 DataFrame 列匹配
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【中文标题】Python/Pandas：如何将字符串列表与 DataFrame 列匹配【英文标题】：Python/Pandas: How to Match List of Strings with a DataFrame column 【发布时间】：2017-07-29 17:22:58 【问题描述】：我想比较两列：Description 和 Employer。我想看看Employer 中的关键字是否在Description 列中找到。我已将Employer 列分解为单词并转换为列表。现在我想看看这些词是否在相应的Description 列中。
示例输入：
print(df.head(25))
Date Description Amount AutoNumber \
0 3/17/2015 WW120 TFR?FR xxx8690 140.00 49246
2 3/13/2015 JX154 TFR?FR xxx8690 150.00 49246
5 3/6/2015 CANSEL SURVEY E PAY 1182.08 49246
9 3/2/2015 UE200 TFR?FR xxx8690 180.00 49246
10 2/27/2015 JH401 TFR?FR xxx8690 400.00 49246
11 2/27/2015 CANSEL SURVEY E PAY 555.62 49246
12 2/25/2015 HU204 TFR?FR xxx8690 200.00 49246
13 2/23/2015 UQ263 TFR?FR xxx8690 102.00 49246
14 2/23/2015 UT460 TFR?FR xxx8690 200.00 49246
15 2/20/2015 CANSEL SURVEY E PAY 1222.05 49246
17 2/17/2015 UO414 TFR?FR xxx8690 250.00 49246
19 2/11/2015 HI540 TFR?FR xxx8690 130.00 49246
20 2/11/2015 HQ010 TFR?FR xxx8690 177.00 49246
21 2/10/2015 WU455 TFR?FR xxx8690 200.00 49246
22 2/6/2015 JJ500 TFR?FR xxx8690 301.00 49246
23 2/6/2015 CANSEL SURVEY E PAY 1182.08 49246
24 2/5/2015 IR453 TFR?FR xxx8690 168.56 49246
26 2/2/2015 RQ574 TFR?FR xxx8690 500.00 49246
27 2/2/2015 UT022 TFR?FR xxx8690 850.00 49246
28 12/31/2014 HU521 TFR?FR xxx8690 950.17 49246
Employer
0 Cansel Survey Equipment
2 Cansel Survey Equipment
5 Cansel Survey Equipment
9 Cansel Survey Equipment
10 Cansel Survey Equipment
11 Cansel Survey Equipment
12 Cansel Survey Equipment
13 Cansel Survey Equipment
14 Cansel Survey Equipment
15 Cansel Survey Equipment
17 Cansel Survey Equipment
19 Cansel Survey Equipment
20 Cansel Survey Equipment
21 Cansel Survey Equipment
22 Cansel Survey Equipment
23 Cansel Survey Equipment
24 Cansel Survey Equipment
26 Cansel Survey Equipment
27 Cansel Survey Equipment
28 Cansel Survey Equipment
我尝试了类似的方法，但它似乎不起作用。：
df['Text_Search'] = df['Employer'].apply(lambda x: x.split(" "))
df['Match'] = np.where(df['Description'].str.contains("|".join(df['Text_Search'])), "Yes", "No")
我想要的输出如下所示：
Date Description Amount AutoNumber \
0 3/17/2015 WW120 TFR?FR xxx8690 140.00 49246
2 3/13/2015 JX154 TFR?FR xxx8690 150.00 49246
5 3/6/2015 CANSEL SURVEY E PAY 1182.08 49246
9 3/2/2015 UE200 TFR?FR xxx8690 180.00 49246
10 2/27/2015 JH401 TFR?FR xxx8690 400.00 49246
11 2/27/2015 CANSEL SURVEY E PAY 555.62 49246
12 2/25/2015 HU204 TFR?FR xxx8690 200.00 49246
13 2/23/2015 UQ263 TFR?FR xxx8690 102.00 49246
14 2/23/2015 UT460 TFR?FR xxx8690 200.00 49246
15 2/20/2015 CANSEL SURVEY E PAY 1222.05 49246
17 2/17/2015 UO414 TFR?FR xxx8690 250.00 49246
19 2/11/2015 HI540 TFR?FR xxx8690 130.00 49246
20 2/11/2015 HQ010 TFR?FR xxx8690 177.00 49246
21 2/10/2015 WU455 TFR?FR xxx8690 200.00 49246
22 2/6/2015 JJ500 TFR?FR xxx8690 301.00 49246
23 2/6/2015 CANSEL SURVEY E PAY 1182.08 49246
24 2/5/2015 IR453 TFR?FR xxx8690 168.56 49246
26 2/2/2015 RQ574 TFR?FR xxx8690 500.00 49246
27 2/2/2015 UT022 TFR?FR xxx8690 850.00 49246
28 12/31/2014 HU521 TFR?FR xxx8690 950.17 49246
29 12/30/2014 WZ553 TFR?FR xxx8690 200.00 49246
32 12/29/2014 JW173 TFR?FR xxx8690 300.00 49246
33 12/24/2014 CANSEL SURVEY E PAY 1219.21 49246
34 12/24/2014 CANSEL SURVEY E PAY 434.84 49246
36 12/23/2014 WT002 TFR?FR xxx8690 160.00 49246
Employer Text_Search Match
0 Cansel Survey Equipment [Cansel, Survey, Equipment] No
2 Cansel Survey Equipment [Cansel, Survey, Equipment] No
5 Cansel Survey Equipment [Cansel, Survey, Equipment] Yes
9 Cansel Survey Equipment [Cansel, Survey, Equipment] No
10 Cansel Survey Equipment [Cansel, Survey, Equipment] No
11 Cansel Survey Equipment [Cansel, Survey, Equipment] Yes
12 Cansel Survey Equipment [Cansel, Survey, Equipment] No
13 Cansel Survey Equipment [Cansel, Survey, Equipment] No
14 Cansel Survey Equipment [Cansel, Survey, Equipment] No
15 Cansel Survey Equipment [Cansel, Survey, Equipment] Yes
17 Cansel Survey Equipment [Cansel, Survey, Equipment] No
19 Cansel Survey Equipment [Cansel, Survey, Equipment] No
20 Cansel Survey Equipment [Cansel, Survey, Equipment] No
21 Cansel Survey Equipment [Cansel, Survey, Equipment] No
22 Cansel Survey Equipment [Cansel, Survey, Equipment] No
23 Cansel Survey Equipment [Cansel, Survey, Equipment] Yes
24 Cansel Survey Equipment [Cansel, Survey, Equipment] No
26 Cansel Survey Equipment [Cansel, Survey, Equipment] No
27 Cansel Survey Equipment [Cansel, Survey, Equipment] No
28 Cansel Survey Equipment [Cansel, Survey, Equipment] No
29 Cansel Survey Equipment [Cansel, Survey, Equipment] No
32 Cansel Survey Equipment [Cansel, Survey, Equipment] No
33 Cansel Survey Equipment [Cansel, Survey, Equipment] Yes
34 Cansel Survey Equipment [Cansel, Survey, Equipment] Yes
36 Cansel Survey Equipment [Cansel, Survey, Equipment] No
【问题讨论】：
没有必要用"|".join(df['Text_Search']) 构造正则表达式，因为pandas 有.isin() 函数。
【参考方案1】：
这里是快速且节省内存的矢量化解决方案，它使用sklearn.feature_extraction.text.CountVectorizer 方法：
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer(min_df=1, lowercase=True)
X = vect.fit_transform(df['Employer'])
cols_emp = vect.get_feature_names()
X = vect.fit_transform(df['Description'])
cols_desc = vect.get_feature_names()
common_cols_idx = [i for i,col in enumerate(cols_desc) if col in cols_emp]
df['Match'] = (X.toarray()[:, common_cols_idx] == 1).any(1)
来源 DF：
In [259]: df
Out[259]:
Date Description Amount AutoNumber Employer
0 3/17/2015 WW120 TFR?FR xxx8690 140.00 49246 Cansel Survey Equipment
2 3/13/2015 JX154 TFR?FR xxx8690 150.00 49246 Cansel Survey Equipment
5 3/6/2015 CANSEL SURVEY E PAY 1182.08 49246 Cansel Survey Equipment
9 3/2/2015 UE200 TFR?FR xxx8690 180.00 49246 Cansel Survey Equipment
10 2/27/2015 JH401 TFR?FR xxx8690 400.00 49246 Cansel Survey Equipment
11 2/27/2015 CANSEL SURVEY E PAY 555.62 49246 Cansel Survey Equipment
12 2/25/2015 HU204 TFR?FR xxx8690 200.00 49246 Cansel Survey Equipment
13 2/23/2015 UQ263 TFR?FR xxx8690 102.00 49246 Cansel Survey Equipment
14 2/23/2015 UT460 TFR?FR xxx8690 200.00 49246 Cansel Survey Equipment
15 2/20/2015 CANSEL SURVEY E PAY 1222.05 49246 Cansel Survey Equipment
17 2/17/2015 UO414 TFR?FR xxx8690 250.00 49246 Cansel Survey Equipment
19 2/11/2015 HI540 TFR?FR xxx8690 130.00 49246 Cansel Survey Equipment
20 2/11/2015 HQ010 TFR?FR xxx8690 177.00 49246 Cansel Survey Equipment
21 2/10/2015 WU455 TFR?FR xxx8690 200.00 49246 Cansel Survey Equipment
22 2/6/2015 JJ500 TFR?FR xxx8690 301.00 49246 Cansel Survey Equipment
23 2/6/2015 CANSEL SURVEY E PAY 1182.08 49246 Cansel Survey Equipment
24 2/5/2015 IR453 TFR?FR xxx8690 168.56 49246 Cansel IR453
26 2/2/2015 RQ574 TFR?FR xxx8690 500.00 49246 Cansel Survey Equipment
27 2/2/2015 UT022 TFR?FR xxx8690 850.00 49246 Cansel Survey Equipment
28 12/31/2014 HU521 TFR?FR xxx8690 950.17 49246 Cansel Survey HU521
结果：
In [261]: df
Out[261]:
Date Description Amount AutoNumber Employer Match
0 3/17/2015 WW120 TFR?FR xxx8690 140.00 49246 Cansel Survey Equipment False
2 3/13/2015 JX154 TFR?FR xxx8690 150.00 49246 Cansel Survey Equipment False
5 3/6/2015 CANSEL SURVEY E PAY 1182.08 49246 Cansel Survey Equipment True
9 3/2/2015 UE200 TFR?FR xxx8690 180.00 49246 Cansel Survey Equipment False
10 2/27/2015 JH401 TFR?FR xxx8690 400.00 49246 Cansel Survey Equipment False
11 2/27/2015 CANSEL SURVEY E PAY 555.62 49246 Cansel Survey Equipment True
12 2/25/2015 HU204 TFR?FR xxx8690 200.00 49246 Cansel Survey Equipment False
13 2/23/2015 UQ263 TFR?FR xxx8690 102.00 49246 Cansel Survey Equipment False
14 2/23/2015 UT460 TFR?FR xxx8690 200.00 49246 Cansel Survey Equipment False
15 2/20/2015 CANSEL SURVEY E PAY 1222.05 49246 Cansel Survey Equipment True
17 2/17/2015 UO414 TFR?FR xxx8690 250.00 49246 Cansel Survey Equipment False
19 2/11/2015 HI540 TFR?FR xxx8690 130.00 49246 Cansel Survey Equipment False
20 2/11/2015 HQ010 TFR?FR xxx8690 177.00 49246 Cansel Survey Equipment False
21 2/10/2015 WU455 TFR?FR xxx8690 200.00 49246 Cansel Survey Equipment False
22 2/6/2015 JJ500 TFR?FR xxx8690 301.00 49246 Cansel Survey Equipment False
23 2/6/2015 CANSEL SURVEY E PAY 1182.08 49246 Cansel Survey Equipment True
24 2/5/2015 IR453 TFR?FR xxx8690 168.56 49246 Cansel IR453 True
26 2/2/2015 RQ574 TFR?FR xxx8690 500.00 49246 Cansel Survey Equipment False
27 2/2/2015 UT022 TFR?FR xxx8690 850.00 49246 Cansel Survey Equipment False
28 12/31/2014 HU521 TFR?FR xxx8690 950.17 49246 Cansel Survey HU521 True
一些解释：
In [266]: cols_desc
Out[266]:
['cansel',
'fr',
'hi540',
'hq010',
'hu204',
'hu521',
'ir453',
'jh401',
'jj500',
'jx154',
'pay',
'rq574',
'survey',
'tfr',
'ue200',
'uo414',
'uq263',
'ut022',
'ut460',
'wu455',
'ww120',
'xxx8690']
In [267]: cols_emp
Out[267]: ['cansel', 'equipment', 'hu521', 'ir453', 'survey']
In [268]: common_cols_idx = [i for i,col in enumerate(cols_desc) if col in cols_emp]
In [269]: common_cols_idx
Out[269]: [0, 5, 6, 12]
In [270]: X.toarray()
Out[270]:
array([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1],
[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1]], dtype=int64)
In [271]: X.toarray()[:, common_cols_idx]
Out[271]:
array([[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 0, 0, 1],
[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 0, 0, 1],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 0, 0, 1],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 0, 0, 1],
[0, 0, 1, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 1, 0, 0]], dtype=int64)
In [272]: X.toarray()[:, common_cols_idx] == 1
Out[272]:
array([[False, False, False, False],
[False, False, False, False],
[ True, False, False, True],
[False, False, False, False],
[False, False, False, False],
[ True, False, False, True],
[False, False, False, False],
[False, False, False, False],
[False, False, False, False],
[ True, False, False, True],
[False, False, False, False],
[False, False, False, False],
[False, False, False, False],
[False, False, False, False],
[False, False, False, False],
[ True, False, False, True],
[False, False, True, False],
[False, False, False, False],
[False, False, False, False],
[False, True, False, False]], dtype=bool)
In [273]: (X.toarray()[:, common_cols_idx] == 1).any(1)
Out[273]: array([False, False, True, False, False, True, False, False, False, True, False, False, False, False, False, True, True, Fals
e, False, True], dtype=bool)
【讨论】：
我需要进一步研究这个解决方案。它看起来是一个非常有创意的解决方案。非常感谢。 @maxu 我喜欢使用 countvectorizer！只是出于好奇 - 您是否根据应用版本计时？ @pansen，不，我还没有这样做——我回家后会尝试“计时”。但我几乎可以肯定，对于更大的 DF，它应该会更快...... ;) PS 如果你愿意，我可以在发布计时结果后对你进行 ping 操作 @maxu 当然谢谢，不要着急。否则我也可以自己安排时间，但我也在旅行 :)【参考方案2】：这是一种解决方案，它将文本拆分为小写集合，并为每一行使用集合交集：
In [160]: x['Match'] = x.Description.str.lower().str.split().map(set).to_frame('desc') \
...: .apply(lambda r: (x.Employer.str.lower().str.split().map(set) & r.desc).any(),
...: axis=1)
...:
In [161]: x
Out[161]:
Date Description Amount AutoNumber Employer Match
0 3/17/2015 WW120 TFR?FR xxx8690 140.00 49246 Cansel Survey Equipment False
2 3/13/2015 JX154 TFR?FR xxx8690 150.00 49246 Cansel Survey Equipment False
5 3/6/2015 CANSEL SURVEY E PAY 1182.08 49246 Cansel Survey Equipment True
9 3/2/2015 UE200 TFR?FR xxx8690 180.00 49246 Cansel Survey Equipment False
10 2/27/2015 JH401 TFR?FR xxx8690 400.00 49246 Cansel Survey Equipment False
11 2/27/2015 CANSEL SURVEY E PAY 555.62 49246 Cansel Survey Equipment True
12 2/25/2015 HU204 TFR?FR xxx8690 200.00 49246 Cansel Survey Equipment False
13 2/23/2015 UQ263 TFR?FR xxx8690 102.00 49246 Cansel Survey Equipment False
14 2/23/2015 UT460 TFR?FR xxx8690 200.00 49246 Cansel Survey Equipment False
15 2/20/2015 CANSEL SURVEY E PAY 1222.05 49246 Cansel Survey Equipment True
17 2/17/2015 UO414 TFR?FR xxx8690 250.00 49246 Cansel Survey Equipment False
19 2/11/2015 HI540 TFR?FR xxx8690 130.00 49246 Cansel Survey Equipment False
20 2/11/2015 HQ010 TFR?FR xxx8690 177.00 49246 Cansel Survey Equipment False
21 2/10/2015 WU455 TFR?FR xxx8690 200.00 49246 Cansel Survey Equipment False
22 2/6/2015 JJ500 TFR?FR xxx8690 301.00 49246 Cansel Survey Equipment False
23 2/6/2015 CANSEL SURVEY E PAY 1182.08 49246 Cansel Survey Equipment True
24 2/5/2015 IR453 TFR?FR xxx8690 168.56 49246 Cansel Survey Equipment False
26 2/2/2015 RQ574 TFR?FR xxx8690 500.00 49246 Cansel Survey Equipment False
27 2/2/2015 UT022 TFR?FR xxx8690 850.00 49246 Cansel Survey Equipment False
28 12/31/2014 HU521 TFR?FR xxx8690 950.17 49246 Cansel Survey Equipment False
PS 这很慢，因为它使用了未矢量化的.apply(..., axis=1) 方法
【讨论】：
【参考方案3】：不同方案的时序比较
让我们准备一个更大的 DF - 2.000 行：
In [3]: df = pd.concat([df] * 10**2, ignore_index=True)
In [4]: df.shape
Out[4]: (2000, 5)
解决方案 1： df.apply(..., axis=1)：
df["Text_Search"] = df.Employer.str.lower().str.split().map(set)
In [15]: %%timeit
...: df.Description.str.lower().str.split().map(set).to_frame('desc') \
...: .apply(lambda r: (df["Text_Search"] & r.desc).any(),
...: axis=1)
...:
1 loop, best of 3: 5.06 s per loop
解决方案 2： CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer(min_df=1, lowercase=True)
In [8]: %%timeit
...: X = vect.fit_transform(df['Employer'])
...: cols_emp = vect.get_feature_names()
...: X = vect.fit_transform(df['Description'])
...: cols_desc = vect.get_feature_names()
...: common_cols_idx = [i for i,col in enumerate(cols_desc) if col in cols_emp]
...: (X.toarray()[:, common_cols_idx] == 1).any(1)
...:
10 loops, best of 3: 88.2 ms per loop
解决方案 3： df.apply(search_func, axis=1)
df["Text_Search"] = df["Employer"].str.lower().str.split()
In [12]: %%timeit
...: df.apply(search_func, axis=1)
...:
1 loop, best of 3: 362 ms per loop
注意：Solution 1 太慢了，所以对于更大的 DF，我不会“计时”这个解决方案
比较df.apply(search_func, axis=1) 和CountVectorizer20.000 行DF：
In [16]: df = pd.concat([df] * 10, ignore_index=True)
In [17]: df.shape
Out[17]: (20000, 6)
In [20]: %%timeit
...: df.apply(search_func, axis=1)
...:
1 loop, best of 3: 3.66 s per loop
In [21]: %%timeit
...: X = vect.fit_transform(df['Employer'])
...: cols_emp = vect.get_feature_names()
...: X = vect.fit_transform(df['Description'])
...: cols_desc = vect.get_feature_names()
...: common_cols_idx = [i for i,col in enumerate(cols_desc) if col in cols_emp]
...: (X.toarray()[:, common_cols_idx] == 1).any(1)
...:
1 loop, best of 3: 825 ms per loop
df.apply(search_func, axis=1) 和 CountVectorizer 的比较对于 200.000 行 DF：
In [22]: df = pd.concat([df] * 10, ignore_index=True)
In [23]: df.shape
Out[23]: (200000, 6)
In [24]: %%timeit
...: df.apply(search_func, axis=1)
...:
1 loop, best of 3: 36.8 s per loop
In [25]: %%timeit
...: X = vect.fit_transform(df['Employer'])
...: cols_emp = vect.get_feature_names()
...: X = vect.fit_transform(df['Description'])
...: cols_desc = vect.get_feature_names()
...: common_cols_idx = [i for i,col in enumerate(cols_desc) if col in cols_emp]
...: (X.toarray()[:, common_cols_idx] == 1).any(1)
...:
1 loop, best of 3: 8.28 s per loop
结论： CountVectorized 解决方案很合适。比 df.apply(search_func, axis=1) 快 4.44 倍
【讨论】：
很好，感谢您的比较！结果如你所料。 +1 干得好，谢谢！我建议将此部分添加到您上面的答案中。这是一个扩展，而不是一个不同的答案。【参考方案4】：这是使用个人search_func 的可读解决方案：
def search_func(row):
matches = [test_value in row["Description"].lower()
for test_value in row["Text_Search"]]
if any(matches):
return "Yes"
else:
return "No"
然后按行应用此函数：
# create example data
df = pd.DataFrame("Description": ["CANSEL SURVEY E PAY", "JX154 TFR?FR xxx8690"],
"Employer": ["Cansel Survey Equipment", "Cansel Survey Equipment"])
print(df)
Description Employer
0 CANSEL SURVEY E PAY Cansel Survey Equipment
1 JX154 TFR?FR xxx8690 Cansel Survey Equipment
# create text searches and match column
df["Text_Search"] = df["Employer"].str.lower().str.split()
df["Match"] = df.apply(search_func, axis=1)
# show result
print(df)
Description Employer Text_Search Match
0 CANSEL SURVEY E PAY Cansel Survey Equipment [cansel, survey, equipment] Yes
1 JX154 TFR?FR xxx8690 Cansel Survey Equipment [cansel, survey, equipment] No
【讨论】：
这个解决方案很快并且对我有用。非常感谢以上是关于Python/Pandas：如何将字符串列表与 DataFrame 列匹配的主要内容，如果未能解决你的问题，请参考以下文章
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